In the present description, a frame is defined as a set of successive items of data and a scene is defined as a set of successive frames. In the particular case of video, a frame is an image.
The quantity of information (entropy) contained in a signal can vary hugely over time. For example, in the case of a video signal, it is possible to pass from a static scene containing smooth textures to a scene comprising many moving objects and complex textures. In this case, a significant increase is observed in the complexity of the scene and in the quantity of information.
When compression techniques are used, this natural variation has two consequences:                first of all, the data bit rate generated by the encoder varies according to the scene;        then, depending on the application strategy chosen, the subjective video quality of the scene at output from the encoder can vary.        
Solutions have been developed to overcome these two problems.
There are known techniques, called bit-rate control techniques used to regulate the output bit rate of the encoder. It is possible to ensure either a constant bit rate (CBR) or a variable bit rate (VBR). In both cases, an external constraint, for example the physical capacity of a communications channel. Is met. If this external constraint varies over time, then this is a variable bit rate (VBR).
Techniques are also known for the dynamic reduction of the entropy of the signal upstream to the encoder. These techniques (which can be used jointly with the above-mentioned bit-rate control techniques) rely on the use, upstream to the encoder, of:                a filtering decision module which provides a filtering setpoint value,        and a filtering module (also called a filter) which filters the signal according to the filtering setpoint value and gives a filtered signal to the encoder.        
A filter is defined as a module that converts the frames of a scene by applying an operator. In the present description, the filters considered are aimed at reducing the entropy of the frames. However, one drawback of these filters is that the video quality of the frames (at output from the encoder) is degraded.
To ensure a minimum subjective video quality at output from the encoder, a first known solution (the simplest solution) is described in a first part (see paragraph 1.2) of the following article: Serhan Uslubas, Ehsan Maani and Aggelos K. Katsaggelos, “A Resolution Adaptive Video Compression System”, (North Western University, Department of EECS).
This first known solution consists of a systematic filtering of the applied filter. In the case of this article, the filter is a resizing (reduction of the encoding resolution).
However, this first known solution is sub-optimal since the resolution chosen could be increased for most of the scenes (the scenes of low complexity are not rendered in full resolution). Conversely if the chosen resolution is too high, it can happen that certain scenes do not reach the minimum level of quality expected at output from the encoder: there is a risk that there will be artifacts of undesirable quality (blockiness, frozen images, etc). It can then be decided to increase the bit rate of the encoder but in this case the efficiency of the compression is limited or an external constraint is not met.
To improve the flexibility of their first approach, the authors of the above-mentioned article propose a second solution described in a second part of the article (see paragraphs 2.1, 2.2 and 2.3). The principle of the second known solution is the following: for each block of each frame, the system carries out two encoding operations, one in high resolution (encoding of the original block) and the other in low resolution (encoding of a filtered block obtained by reducing the resolution). Then, the system selects one of the two encodings in taking the rate-distortion (RD) cost as the criterion.
This second known solution is efficient but not optimal for the following reasons:                the proposed system integrates a double encoding, which remains a costly solution. Furthermore, the decision on resolution is taken for each block of each frame and the criterion used (rate-distortion (RD) cost) has a cost which is high. This second solution significantly increases the encoding time;        in a same frame, blocks are encoded in full resolution (high resolution) and others have undergone resizing. This process compromises the homogeneity of the quality of the frame at output from the encoder.        
A third known solution is described in the article by Jie Dong and Yan Ye, “Adaptive Downsampling for High-Definition Video Coding”, (InterDigital Communications, San Diego, Calif.). It improves the second known solution by applying a resizing (downsampling) of the entire frame, the chosen resizing being the one that gives the best balance between two estimated distortions (distortion related to encoding and distortion related to resizing), and therefore that which achieves the best overall performance in terms of rate-distortion (RD) cost.
It thus resolves the problem of homogeneity of filtering. However, the third known solution is not complete for the following reasons:                the module which gives optimal resolution cannot be used to obtain it automatically. The authors do not propose a generic solution that can be carried out on a real-time product but a case-by-case solution;        the formula for estimating the distortion related to the encoding is theoretical: it is not re-applicable because the way in which the parameters are obtained is unexplained.        